{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ANTsPy Tutorial\n",
    "\n",
    "In this tutorial, I will show of some of the core ANTsPy functionality. I will highlight the similarities with ANTsR."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic IO, Processing, & Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import ants\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1On72s5/h3//933HnnXdi69at8Pv98lp+0XK4ePEinnrqKUxMTGB+fh4OhwOb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\nVgIfBE8bZBpFIiKPiMh+Edl/1VIbhjGvzFoxiEgz8AzwNefceeBRYD1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117b3e0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = ants.image_read( ants.get_ants_data('r16'), 'float' )\n",
    "plt.imshow(img.numpy(), cmap='Greys_r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EUbMOhsF/RbuqTnW3Z4BvstQFe+Fcl7G7PTO7Cn/PSnUNbjnXQL+2v9wlBhjgcu37Ugiz\nDoYfAlcluTLJxSxdK/LIjGv6nSSXdNe5JMklwHtY+nr5EWB/N9t+4J7ZVNhYqa4jwPu7UfTrgV+O\ndI1nYohf21/pEgMMbLmuVOdUl+lGjKKuMsJ6M0ujqj8BPjnres6r7U0sjeb+CHj8XH3AHwEPAseB\nB4BtM6jtKyx1F19kaZ/xAyvVxdKo+T91y/hRYM8Aav2XrpZHuhV3x8j8n+xqfQq4aQPrfDtLuwmP\nAMe6n5uHtlwvUOfUlqlnPkpqzHpXQtIAGQySGgaDpIbBIKlhMEhqGAySGgaDpIbBIKnx/3qfp4JO\n12+QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117c6e470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = ants.get_mask(img)\n",
    "plt.imshow(mask.numpy())\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# N4 Bias Correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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paQnpdBqhUAijo6MDmgIAkmnggs1ms2g2mxJyGIaBVqslKUu/3y8FUY1GA7lcDnv37sXn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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1189a46d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_n4 = ants.n4_bias_field_correction(img, shrink_factor=3)\n",
    "\n",
    "plt.imshow(img_n4.numpy(), cmap='Greys_r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overloaded Mathematical Operators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gXACm6QiztgiB/0YukRK179ddBA3MLAMfONnHBothERQXAdVXmAqnpegtP2Gu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\nxgQTBsMwJpgwGIYx4f8ANe8PDUxaD9oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118accb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff = img - img_n4\n",
    "plt.imshow(diff.numpy())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Atropos\n",
    "\n",
    "The following example has been validated with ANTsR. That is, both ANTsR and ANTsPy return the EXACT same result (images).\n",
    "\n",
    "R Version:\n",
    "```R\n",
    "img <- antsImageRead( getANTsRData(\"r16\") , 2 )\n",
    "img <- resampleImage( img, c(64,64), 1, 0 )\n",
    "mask <- getMask(img)\n",
    "segs1 <- atropos( a = img, m = '[0.2,1x1]',\n",
    "   c = '[2,0]',  i = 'kmeans[3]', x = mask )\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'segmentation': <ants.core.io.ANTsImage object at 0x11840f400>, 'probabilityimages': [<ants.core.io.ANTsImage object at 0x118797710>, <ants.core.io.ANTsImage object at 0x118479390>, <ants.core.io.ANTsImage object at 0x11840f9e8>]}\n"
     ]
    }
   ],
   "source": [
    "img = ants.image_read( ants.get_ants_data(\"r16\") ).clone('float')\n",
    "img = ants.resample_image( img, (64,64), 1, 0 )\n",
    "mask = ants.get_mask(img)\n",
    "segs1 = ants.atropos( a = img, m = '[0.2,1x1]', \n",
    "                     c = '[2,0]',  i = 'kmeans[3]', x = mask )\n",
    "\n",
    "print(segs1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qNoPkNJL1JOsbsLcyWgshDopyDL8GwHgAt5vZyQB2ITGst2I5nhZL8pjZDDOr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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1184f7d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mUNXRAEYDka7+PllLCEkIcTm+qq73/i8UkekAugDYJCJNVXWDiDQFUJhEO0mA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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1184167f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2K/QBUD+SqR++He3HPmbWvq5GeQ38cKdkuZmlWhcQqz6oH+pHQ/VDp/oiMaTA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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118413ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    plt.imshow(segs1['probabilityimages'][i].numpy())\n",
    "    plt.title('Class %i' % i)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mi0BK4i8FPgV8OyIuAX5FX7M+eqtyzLkyh6QpSdOSpo9zbNR4zawBKYl/ADgQEbuLxw/R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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118162358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(segs1['segmentation'].numpy())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Registration\n",
    "\n",
    "R Version:\n",
    "```R\n",
    "fi <- antsImageRead(getANTsRData(\"r16\") )\n",
    "mi <- antsImageRead(getANTsRData(\"r64\") )\n",
    "fi<-resampleImage(fi,c(60,60),1,0)\n",
    "mi<-resampleImage(mi,c(60,60),1,0) # speed up\n",
    "mytx <- antsRegistration(fixed=fi, moving=mi, typeofTransform = c('SyN') )\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'warpedmovout': <ants.core.io.ANTsImage object at 0x118123fd0>, 'warpedfixout': <ants.core.io.ANTsImage object at 0x1181234e0>, 'fwdtransforms': ['/var/folders/__/v7ryn14n0x749jd36ttj_s380000gp/T/tmphqe1sm5v1Warp.nii.gz', '/var/folders/__/v7ryn14n0x749jd36ttj_s380000gp/T/tmphqe1sm5v0GenericAffine.mat'], 'invtransforms': ['/var/folders/__/v7ryn14n0x749jd36ttj_s380000gp/T/tmphqe1sm5v0GenericAffine.mat', '/var/folders/__/v7ryn14n0x749jd36ttj_s380000gp/T/tmphqe1sm5v1InverseWarp.nii.gz']}\n"
     ]
    }
   ],
   "source": [
    "fi = ants.image_read( ants.get_ants_data('r16') ).clone('float')\n",
    "mi = ants.image_read( ants.get_ants_data('r64')).clone('float')\n",
    "fi = ants.resample_image(fi,(60,60),1,0)\n",
    "mi = ants.resample_image(mi,(60,60),1,0)\n",
    "mytx = ants.registration(fixed=fi, moving=mi, \n",
    "                         type_of_transform = 'SyN' )\n",
    "\n",
    "print(mytx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1pXKzi5lDjbvq+m/8Q8icK+ecw1/g6A02fGTaUF8vlzRkB6SZUFVnhtYLSouF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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118169eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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i3V6/EEDXuyJ+zGmrlBtfDnusWNT/8zeLvbLfNQUAY5Kk6y/Bt/llfWBZ7oTA\nuQvrTBbb9w6MErJM3/LfxspkIQsPkWG5V3/ObL6x/EG5DPdIYJPPtnbTV/xs2rNCtqFBDvpq2s19\nUwJ2vN+u398m5zmCy3J3+TbffHzvJ4XsghzpHr0y34RSP1Z/PpTO6bbHZ+Y7mLmQmYsBXAPgLWb+\nEoC3AXQEiM8EML+Ll1AUpZ9xIn782wDcQkRb4dj8T/ROkxRF6WuOa3UeM78D4B23vB3ApN5vkqIo\nfY2G7PYGwSWdATb/3mx2mZghbd0XJs8R9XrfTjG5IZmmq5nlAM3v119weLyQ1bSYidTpudJ/vaJW\n+vU/2G/qldtkTMLU8+SmlFuqjW/8YJ0Mrf3F2BdF3b8Lz7Gy4+YGshJvbJZLb1fXDfPKczecKWRn\nFMqppQEJ5vM9JCOK0VYt5w4+jmjIrqIoXaKKrygWohl4eoNuzKXRXzf7yDe/KcN5lwc2dpxfYVaf\nfb3gHSEriq8S9bQ4YwoMTZYuxfx0c+6c8oALbLDcfGNq0Tqv/KddU4SsqV3+RPxZbYoz5T23BYbo\nk8IfeeXg+3xur1n196PhrwhZQSCTz092TDWVfdK9WLZ/pKiX/NmE8HL1Oiidoz2+oliIKr6iWIgq\nvqJYiNr40cA3BxC+UtrFjz0vw0rj48wS1NvXXClkd49dIOph36aaQxNltpmQbylrZmD3nrVH5c4/\nZRuMuyx5r/xJrC6SYbhV60xI75mf3iVkQxNkGzb63I3zDkwUsi8VmOW/iZCuvueOyKw6D51usv68\nP6JEyJ55VrqHeZXa9ZGgPb6iWIgqvqJYiA71o0x7jcwYk/1F+eytO9+suKu/UMpWFheL+pQ0szKt\nKDDMrmo3brfpeWVCtqRKruRDnDFFRl28XYjGpcsVgZWDTMTdRZlyg4/skNxz/o51ZoPLx8/4k5Bt\nazERgNXt0kX36qZxov5m4hiv3NIsf7IjfvY+lONHe3xFsRBVfEWxEFV8RbEQtfFjTFuV3NwivGCZ\nV46ffK6Q/fuodK2V1xvX2jWDZHbcX269xCtPL1wjZGMH7BP15AkmK1BOglwpVxw+JOqzso1NfbBN\nZvZ5ZL90rTW1mJ9XZWCTjB+vu9wrZyQ3CtmlJRtEfcPtxuaPX9yjDHBKAO3xFcVCVPEVxUJU8RXF\nQtTG78cU/0huYNk+9hRRX/U5kzlnxJXSFq9tNBl4wnEys++H1XJp8LJ3TjX3KJbhvQ9Nek7Ur1r9\nVa9ckCGbfO/qAAAF60lEQVTnJy4cuEnUb5i4xCtXBjLpnldgluwumSfDebf9SsYOxDd9CKV30R5f\nUSxEFV9RLESTbX5MoHhptdXOOMsrT/7+ciFbdZscWicsMi6y9sVFQjYha7eo17UZE+LyLJnEc3uT\nzMDzwm6TGPPQe/lCVvy7jV45uCHJ/0pe+jHdy74v0GSbiqJ0iSq+oliIKr6iWIja+IogLiyXyMYN\nHiTqe6YXeuXB/5DuvGNmvznWpiNqw/caauMritIlEQXwEFE5gBoAbQBambmUiLIBPA+gGEA5gKuZ\n+UhXr6EoSv/heHr8TzPzBGbu2AjudgCLmbkEwGK3rijKScCJhOzOAHCBW34Kzi66t51ge5QY094o\nl8i2l+8U9bxfm/pxWeZqx/crIu3xGcCbRLSSiGa7x/KYuWNh934AeZ1dSESziWgFEa1oQVNnpyiK\nEmUi7fHPZ+Y9RDQIwEIi2ugXMjMTUaePdGaeA2AO4Mzqn1BrFUXpFSLq8Zl5j/u/AsA8AJMAHCCi\nfABw/1f0VSMVReldulV8IkolorSOMoDPAFgL4GUAM93TZgKY31eNVBSld4lkqJ8HYB45ARjxAP7C\nzK8T0XIAc4loFoAdAK7uu2YqitKbdKv4zLwdwPhOjlcC0DA8RTkJ0cg9RbEQVXxFsRBVfEWxEFV8\nRbEQVXxFsRBVfEWxEFV8RbEQVXxFsRBVfEWxEFV8RbEQVXxFsRBVfEWxEFV8RbEQVXxFsRBVfEWx\nEFV8RbEQVXxFsRBVfEWxEFV8RbEQVXxFsRBVfEWxEFV8RbEQVXxFsRBVfEWxEFV8RbEQVXxFsRBV\nfEWxEFV8RbEQVXxFsRBi5ujdjOggnC21BwI4FLUbd4+259j0t/YA/a9N/aU9w5g5t7uToqr43k2J\nVjBzadRv3AXanmPT39oD9L829bf2dIcO9RXFQlTxFcVCYqX4c2J0367Q9hyb/tYeoP+1qb+155jE\nxMZXFCW26FBfUSxEFV9RLCSqik9ElxHRJiLaSkS3R/Pevjb8gYgqiGit71g2ES0koi3u/6wotqeI\niN4movVEtI6Ivh3LNhFRmIiWEVGZ25573OPDiWip+909T0SJ0WiPr10hIlpFRAti3R4iKieiNUS0\nmohWuMdi9hvqCVFTfCIKAfgNgM8COA3AtUR0WrTu7+NJAJcFjt0OYDEzlwBY7NajRSuAW5n5NADn\nALjR/Vxi1aYmABcy83gAEwBcRkTnALgfwK+YeRSAIwBmRak9HXwbwAZfPdbt+TQzT/D57mP5Gzp+\nmDkqfwDOBfCGr34HgDuidf9AW4oBrPXVNwHId8v5ADbFol3u/ecDuKQ/tAlACoAPAUyGE5UW39l3\nGYV2FMJRpgsBLABAMW5POYCBgWMx/76O5y+aQ/0CALt89d3usf5AHjPvc8v7AeTFohFEVAxgIoCl\nsWyTO6xeDaACwEIA2wBUMXOre0q0v7sHAXwPQLtbz4lxexjAm0S0kohmu8f6xW8oUuJj3YD+BjMz\nEUXdx0lEAwD8FcDNzFxNRDFrEzO3AZhARJkA5gEYE617ByGiaQAqmHklEV0Qq3YEOJ+Z9xDRIAAL\niWijXxir39DxEM0efw+AIl+90D3WHzhARPkA4P6viObNiSgBjtI/w8wv9Yc2AQAzVwF4G85QOpOI\nOjqKaH53UwBMJ6JyAM/BGe4/FMP2gJn3uP8r4DwYJ6EffF/HQzQVfzmAEnc2NhHANQBejuL9j8XL\nAGa65Zlw7OyoQE7X/gSADcz8y1i3iYhy3Z4eRJQMZ75hA5wHwFXRbg8z38HMhcxcDOc38xYzfylW\n7SGiVCJK6ygD+AyAtYjhb6hHRHNCAcBUAJvh2Iw/iMWkBoBnAewD0ALHNpwFx2ZcDGALgEUAsqPY\nnvPh2Iz/BrDa/ZsaqzYBOAPAKrc9awHc6R4fAWAZgK0AXgCQFIPv7gIAC2LZHve+Ze7fuo7fcSx/\nQz3505BdRbEQjdxTFAtRxVcUC1HFVxQLUcVXFAtRxVcUC1HFVxQLUcVXFAv5HxlkEUfFpR5DAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1180baeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XdbgKQKeInHr8NgCYe7oXklxPchvJbQkMnu4UwzByzKiKT/IDAFpEZPt4biAi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\nYopvGCHEFN8wQogpvmGEkP8PUQBIZc1cd1IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117e97128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(mi.numpy())\n",
    "plt.title('Original moving image')\n",
    "plt.show()\n",
    "\n",
    "plt.imshow(fi.numpy())\n",
    "plt.title('Original fixed image')\n",
    "plt.show()\n",
    "\n",
    "plt.imshow(mytx['warpedmovout'].numpy())\n",
    "plt.title('Warped moving imag')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SparseDecom2\n",
    "\n",
    "Another ANTsR-validated result:\n",
    "\n",
    "```R\n",
    "mat<-replicate(100, rnorm(20))\n",
    "mat2<-replicate(100, rnorm(20))\n",
    "mat<-scale(mat)\n",
    "mat2<-scale(mat2)\n",
    "mydecom<-sparseDecom2(inmatrix = list(mat,mat2), sparseness=c(0.1,0.3), nvecs=3, its=3, perms=0)\n",
    "```\n",
    "The 3 correlation values from that experiment are: [0.9762784, 0.9705170, 0.7937968]\n",
    "\n",
    "After saving those exact matrices, and running the ANTsPy version, we see that we get the exact same result "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available Results:  ['projections', 'projections2', 'eig1', 'eig2', 'corrs']\n",
      "Correlations:  [ 0.97627841  0.97051702  0.79379676]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "mat = pd.read_csv('~/desktop/mat.csv', index_col=0).values\n",
    "mat2 = pd.read_csv('~/desktop/mat2.csv', index_col=0).values\n",
    "\n",
    "\n",
    "mydecom = ants.sparseDecom2(inmatrix=(mat,mat2), sparseness=(0.1,0.3), \n",
    "                            nvecs=3, its=3, perms=0)\n",
    "\n",
    "print('Available Results: ', list(mydecom.keys()))\n",
    "print('Correlations: ', mydecom['corrs'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
